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    <identifier>10.57760/sciencedb.09602</identifier>
    <datestamp>2023-11-10T15:54:28Z</datestamp>
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<oai_dc:dc xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:date>2023-11-10</dc:date>
  <dc:title>DGraph-Fin</dc:title>
  <dc:identifier>doi:10.57760/sciencedb.09602</dc:identifier>
  <dc:language>en</dc:language>
  <dc:description>This dataset is set against the background of intelligent risk control and includes a directed dynamic graph that is desensitized and has fraudulent users, reflecting the social network relationships of credit users. Sample data from different time periods of actual business, where node A is an internet credit user, and the directed edge from node A to node B represents user A recording user B as an emergency contact; The type of edge represents the classification of emergency contacts, and the edge attribute is temporal information (desensitized to a positive integer starting from 1, in days) to form a heterogeneous dynamic graph.&amp;nbsp;The network dataset contains over 3.7 million nodes and is divided into the following three categories, with specific definitions as follows:&amp;nbsp;(1) Fraudulent users: have experienced credit overdue and fraudulent behavior in their past lending behavior; Marked as positive samples, including node 15509, accounting for 0.42%;&amp;nbsp;(2) Normal users: have never experienced credit overdue or fraudulent behavior in their past lending behavior; Marked as negative samples, including nodes 1210092, accounting for 32.7%;&amp;nbsp;(3) Background node: There has been no past lending behavior, and unmarked is not used as a fraud detection target. It is only used to supplement the connectivity and neighborhood background information of social networks, including a total of 2474949 nodes, accounting for 66.9%;&amp;nbsp;The nodes contain 17 dimensional desensitized attribute vectors, each corresponding to different elements of attribute information, and the missing values are supplemented with -1 to the corresponding attribute vectors. In consideration of privacy protection, only the category of the attribute is provided in the original data, as shown below:&amp;nbsp;(1) User ID: The unique identification number corresponding to the user's credit account;&amp;nbsp;(2) Basic personal information: This includes basic personal identity information such as the user's age and gender;&amp;nbsp;(3) Communication method: Information related to contact information such as phone numbers;&amp;nbsp;(4) Lending behavior: This includes information such as &amp;quot;repayment maturity date&amp;quot; and &amp;quot;actual repayment date&amp;quot; to describe user lending behavior;&amp;nbsp;(5) Emergency contact information: This includes the basic information of the emergency contact provided during credit account registration, such as the name, contact information, and final update date of the emergency contact person;&amp;nbsp;At the same time, the network dataset contains over 4.3 million edges, which are divided into 11 categories based on the categories of emergency contacts,&amp;nbsp;It corresponds to the &amp;quot;emergency contact information&amp;quot; in the node attribute, so for privacy protection reasons, the original dataset is represented by 1 to 11, and the actual direction of the undisclosed category&amp;nbsp;</dc:description>
  <dc:subject>Social Network; Fraud Detection; Unbalanced </dc:subject>
  <dc:creator>Lanze Zhang</dc:creator>
  <dc:rights>PUBLIC</dc:rights>
  <dc:rights>https://api.github.com/licenses/unlicense</dc:rights>
  <dc:type>dataset</dc:type>
  <dc:publisher>Science Data Bank</dc:publisher>
</oai_dc:dc>

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